吉林大学学报（理学版）2025，Vol.63Issue(2)：437-444,8.DOI:10.13413/j.cnki.jdxblxb.2023474
基于轻量级注意力残差网络的面部表情识别方法
Facial Expression Recognition Method Based on Lightweight Attention Residual Network
摘要
Abstract
Aiming at the problems of a large number of parameters and the long training time of convolutional neural networks,we proposed a facial expression recognition method based on a lightweight attention residual network.Firstly,we rebuilt the model by using the residual network as a skeleton,and improved the model performance by reducing the number of layers and improving the residual module.Secondly,the depthwise separable convolution was introduced to reduce the number of model parameters and computational effort.Finally,the squeeze and excitation module of ReLU function was replaced by Mish function to adaptively adjust the channel weight.The model was validated by using the classical ten-fold cross-validation mode on two public datasets CK+and JAFFE,and obtained accuracies of 98.16%and 96.67%,respectively.The experimental results show that the proposed method provides a better trade-off between model identification accuracy and complexity.关键词
面部表情识别/轻量级/残差网络/深度可分离卷积/注意力机制Key words
facial expression recognition/lightweight/residual network/depthwise separable convolution/attention mechanism分类
信息技术与安全科学引用本文复制引用
郜高飞,邵党国,马磊,易三莉..基于轻量级注意力残差网络的面部表情识别方法[J].吉林大学学报（理学版）,2025,63(2):437-444,8.基金项目
国家自然科学基金(批准号:62266025)和云南省计算机技术应用重点实验室开放基金(批准号:CB22144S078A). （批准号:62266025）
